... causal inference, A/B testing, and offline evaluation frameworks to measure and improve model ... internships. • Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow ...
... causal inference, A/B testing, and offline evaluation frameworks to measure and improve model ... internships. • Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow ...
Machine Learning Engineer, Next-Generation Recommendation Systems
New York, NY · On-site +1
$127K - $191K/yr
Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation ... Industry experience in ads, recommendation, or user understanding systems (internship experience ...
Machine Learning Engineer, Next-Generation Recommendation Systems
New York, NY · On-site +1
$127K - $191K/yr
Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation ... Industry experience in ads, recommendation, or user understanding systems (internship experience ...
You will also help craft how we measure AI-powered features through causal inference and ... Snowflake) through research or internships * Ability to communicate technical concepts clearly to ...
You will also help craft how we measure AI-powered features through causal inference and ... Snowflake) through research or internships * Ability to communicate technical concepts clearly to ...
Product Data Analyst
New York, NY · Remote
$145K - $175K/yr
... internships) * SQL fluency -- complex joins, incremental computation, window functions, query ... Causal inference methods (diff-in-diff, regression discontinuity, propensity matching) * Prior work ...
Quick apply
Product Data Analyst
New York, NY · Remote
$145K - $175K/yr
... internships) * SQL fluency -- complex joins, incremental computation, window functions, query ... Causal inference methods (diff-in-diff, regression discontinuity, propensity matching) * Prior work ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
... causal inference. Search & Discovery ML : The Search and Discovery ML team at Instacart works ... Past internship contributions include: Tensor-based complementary recommendations, published at ...
Manager, Biostatistics
Tarrytown, NY · Hybrid
... other methods, for causal inference. The incumbent will co-author any research protocols ... internship experience, * MS degree in statistics/biostatistics with >7 years' experience in the ...
Manager, Biostatistics
Tarrytown, NY · Hybrid
... other methods, for causal inference. The incumbent will co-author any research protocols ... internship experience, * MS degree in statistics/biostatistics with >7 years' experience in the ...
Manager, Biostatistics
Tarrytown, NY · On-site
... other methods, for causal inference. The incumbent will co-author any research protocols ... internship experience, * MS degree in statistics/biostatistics with >7 years' experience in the ...
Manager, Biostatistics
Tarrytown, NY · On-site
... other methods, for causal inference. The incumbent will co-author any research protocols ... internship experience, * MS degree in statistics/biostatistics with >7 years' experience in the ...
Internship Causal Inference information
What types of projects and team collaborations can I expect during an Internship in Causal Inference?
As an intern in Causal Inference, you will typically work on projects focused on analyzing data to determine cause-and-effect relationships, such as assessing the impact of interventions or policy changes. You may collaborate with data scientists, statisticians, and domain experts, contributing to experimental design, data cleaning, and the application of statistical methods. Interns often participate in weekly team meetings, present findings, and receive mentorship from senior researchers. This hands-on experience provides valuable exposure to both technical skills and interdisciplinary teamwork, which are crucial for growth in quantitative research roles.
What are the key skills and qualifications needed to thrive as an Internship Causal Inference, and why are they important?
To thrive in an Internship Causal Inference role, you need a solid background in statistics, econometrics, and data analysis, typically supported by coursework or degrees in statistics, economics, or related quantitative fields. Familiarity with statistical programming languages such as R or Python, and experience with causal inference frameworks and tools like propensity score matching or regression discontinuity, are commonly required. Strong problem-solving abilities, attention to detail, and effective communication skills help interns interpret results and collaborate with research teams. These skills and qualities are essential to ensure rigorous and meaningful analysis that informs data-driven decisions.
What is the difference between Internship Causal Inference vs Data Analyst?
| Aspect | Internship Causal Inference | Data Analyst |
|---|---|---|
| Required Credentials | Undergraduate or graduate in statistics, economics, or related fields | Degree in statistics, data science, or related fields |
| Work Environment | Research-focused, often in academia or research institutions | Business, corporate, or consulting settings |
| Employer & Industry Usage | Universities, research labs, tech companies | Finance, marketing, healthcare, tech companies |
| Comparison Search Intent | Understanding causal inference techniques during internships | Analyzing data to inform business decisions |
Internship Causal Inference roles focus on applying statistical methods to identify cause-effect relationships, often in research settings. Data Analyst roles involve interpreting data to support business strategies. While both require analytical skills, causal inference internships emphasize research and advanced statistical techniques, whereas data analyst positions focus on data processing and reporting.
What is an Internship in Causal Inference?
An Internship in Causal Inference is a temporary position, typically for students or early-career professionals, that focuses on learning and applying methods to determine cause-and-effect relationships in data. Interns in this field work with statistical models, experimental designs, and software tools to analyze data and infer causal relationships, often in fields like economics, public health, or data science. These internships provide hands-on experience with real-world datasets, mentorship from experienced researchers, and opportunities to contribute to ongoing projects. Participants gain valuable skills in programming, statistical analysis, and research methodology, which are highly sought after in both academia and industry.
What are the most commonly searched types of Causal Inference jobs in New York? The most popular types of Causal Inference jobs in New York are:
What are popular job titles related to Internship Causal Inference jobs in New York? For Internship Causal Inference jobs in New York, the most frequently searched job titles are:
What job categories do people searching Internship Causal Inference jobs in New York look for? The top searched job categories for Internship Causal Inference jobs in New York are:
What cities in New York are hiring for Internship Causal Inference jobs? Cities in New York with the most Internship Causal Inference job openings:
Full-time
Posted 24 days ago
Job description
Job Summary:
Unity's Vector AI team builds machine learning systems for ad targeting across billions of users. They are seeking a Machine Learning Engineer to develop next-generation recommendation systems that leverage advanced techniques such as reinforcement learning and large language models.
Responsibilities:
• Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.
• Develop user understanding systems — conversion prediction, behavioral modeling, and value estimation — that operate across billions of impressions.
• Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
• Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
• Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.
• Communicate findings clearly to technical and non-technical stakeholders across engineering, product, and business teams.
Qualifications:
Required:
• PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate).
• Strong research foundations in one or more of: recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization.
• Experience working with large-scale data and ML systems, whether through research or industry internships.
• Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• A track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.
• Strong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiences.
Preferred:
• Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
• Hands-on experience with production ML pipelines — training at scale, feature engineering, or experimentation infrastructure.
• Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems.
• Familiarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworks.
• Exposure to causal inference, uplift modeling, or A/B testing at scale.
• Genuine curiosity about applied research and the drive to see ideas through to impact.
Company:
Unity [NYSE: U] offers a suite of tools to create, market, and grow games and interactive experiences across all major platforms from mobile, PC, and console, to extended reality. Founded in 2004, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.
Unity's Vector AI team builds machine learning systems for ad targeting across billions of users. They are seeking a Machine Learning Engineer to develop next-generation recommendation systems that leverage advanced techniques such as reinforcement learning and large language models.
Responsibilities:
• Design, build, and evaluate next-generation ranking and recommendation models that incorporate LLMs, RLHF, and preference learning to improve ad relevance and user experience.
• Develop user understanding systems — conversion prediction, behavioral modeling, and value estimation — that operate across billions of impressions.
• Apply reinforcement learning and optimization techniques to bidding strategy, auction dynamics, and real-time ad delivery.
• Design and run rigorous experiments using causal inference, A/B testing, and offline evaluation frameworks to measure and improve model quality.
• Partner with engineering to bring research ideas into production, working across the full pipeline from training data to deployed model.
• Communicate findings clearly to technical and non-technical stakeholders across engineering, product, and business teams.
Qualifications:
Required:
• PhD in Computer Science, Machine Learning, Statistics, or a related field (graduating 2026 or recent graduate).
• Strong research foundations in one or more of: recommendation systems, reinforcement learning, LLM post-training or alignment, human-AI collaboration, probabilistic modeling, or optimization.
• Experience working with large-scale data and ML systems, whether through research or industry internships.
• Fluency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• A track record of rigorous, high-quality research — publications at top venues (NeurIPS, ICML, ICLR, KDD, RecSys, ACL, WWW, or similar) are a strong signal.
• Strong written and verbal communication skills — able to make complex ideas accessible across technical and non-technical audiences.
Preferred:
• Industry experience in ads, recommendation, or user understanding systems (internship experience counts).
• Hands-on experience with production ML pipelines — training at scale, feature engineering, or experimentation infrastructure.
• Experience applying LLMs or generative models to ranking, retrieval, or structured prediction problems.
• Familiarity with agentic AI approaches — multi-step reasoning, tool use, or human-AI collaboration frameworks.
• Exposure to causal inference, uplift modeling, or A/B testing at scale.
• Genuine curiosity about applied research and the drive to see ideas through to impact.
Company:
Unity [NYSE: U] offers a suite of tools to create, market, and grow games and interactive experiences across all major platforms from mobile, PC, and console, to extended reality. Founded in 2004, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.